A Hierarchical Energy Management Strategy for Power-Split Plug-in Hybrid Electric Vehicles Considering Velocity Prediction

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Abstract
In this paper, a hierarchical energy management strategy is proposed to achieve optimal energy distribution in plug-in hybrid electric vehicles by dividing the energy management algorithm into two layers. Between two control layers, a novel velocity-prediction method based on wavelet transformation and a radial basis function neural network is introduced to realize accurate vehicle speed prediction. To simplify the problem, a quadratic optimization method is employed to find the optimal state-of-charge (SOC) trajectory in the upper layer and the calculation time can be minimized to be within 400 ms. Model predictive control is established simultaneously in the bottom layer to achieve fast, and local energy management based on the predicted velocity and the planned SOC trajectory. Simulation results show that the proposed method can increase the accuracy of the velocity prediction and improve the fuel economy with a fast calculation speed.
Funding Information
  • National Key R&D Program of China (2018YFB0104000, 2018YFB0104900)
  • National Science Foundation (61763021, 51764029)

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